Data Scientist (Jr./Sr./Principal)

4 - 9 years

35.0 - 65.0 Lacs P.A.

Gurgaon

Posted:3 months ago| Platform: Naukri logo

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Skills Required

PytorchPythonSQLTensorflowAirflowNLPGitAWSMachine Learning

Work Mode

Work from Office

Job Type

Full Time

Job Description

e.g.andmodellingDesignation : Principal(9+ Yrs)/Sr.(5+ Yrs)/Jr.(3+ Yrs) Data Scientist Responsibilities: • Perform as a Data Scientist by leading execution and technical planning within the organization and team, along with mentoring other Data Scientists. • Ideate and implement discoveries to build new product features and data products, and improve existing methodologies and algorithms. • Collaborate across Product, Data, and Engineering teams to identify, investigate, integrate, and deliver solutions Reback-end and front-end data services solution slated to back-end and front-end data services. • Lead and deliver complex projects involving data and statistical modelling (e. g. sampling, forecasting, segmentation, classification, predictive modeling, etc. ) • Regularly communicate outcomes, new initiatives, improvements, etc. to stakeholders. • Serve as a data science subject matter expert for both internal and external stakeholders. Requirements: • Strong expertise in data analytics and interpretation skills. • Proven ability to design analytical solutions to complex business problems and to partner with cross-functional peers to tailor those solutions for the greatest possible impact. • Deep understanding of ML domains (e.g. classification algorithms, NLP, computer vision, recommendation, anomaly detection, graph learning), algorithms (e.g. deep learning, gradient boosted trees, optimization), and best practices, (e. g. skew minimization, A/B testing, feature engineering, model selection) • Strong programming (Python / Scala / Java / C++ or equivalent) or data engineering skills. • Utilize OSS ML technologies such as PyTorch, TensorFlow, Spark, and Airflow. • Versatility to communicate clearly with both technical and non-technical audiences. • Publications or presentations in recognized Machine Learning and Data Mining journals/conferences are a plus.

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